def runTracker(self): foregroundPointsNum = 0 while True: frame = self.render.getNextFrame() frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if len(self.tracks) > 0: img0, img1 = self.prev_gray, frame_gray p0 = np.float32([tr[-1][0] for tr in self.tracks]).reshape(-1, 1, 2) p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params) p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params) d = abs(p0-p0r).reshape(-1, 2).max(-1) good = d < 1 new_tracks = [] for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good): if not good_flag: continue tr.append([(x, y), self.frame_idx]) if len(tr) > self.track_len: del tr[0] new_tracks.append(tr) self.tracks = new_tracks if self.frame_idx % self.detect_interval == 0: goodTracksCount = 0 for tr in self.tracks: oldRect = self.render.getRectInTime(self.render.timeStep * tr[0][1]) newRect = self.render.getRectInTime(self.render.timeStep * tr[-1][1]) if isPointInRect(tr[0][0], oldRect) and isPointInRect(tr[-1][0], newRect): goodTracksCount += 1 if self.frame_idx == self.detect_interval: foregroundPointsNum = goodTracksCount fgIndex = float(foregroundPointsNum) / (foregroundPointsNum + 1) fgRate = float(goodTracksCount) / (len(self.tracks) + 1) if self.frame_idx > 0: self.assertGreater(fgIndex, 0.9) self.assertGreater(fgRate, 0.2) mask = np.zeros_like(frame_gray) mask[:] = 255 for x, y in [np.int32(tr[-1][0]) for tr in self.tracks]: cv2.circle(mask, (x, y), 5, 0, -1) p = cv2.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params) if p is not None: for x, y in np.float32(p).reshape(-1, 2): self.tracks.append([[(x, y), self.frame_idx]]) self.frame_idx += 1 self.prev_gray = frame_gray if self.frame_idx > 300: break
def runTracker(self): foregroundPointsNum = 0 while True: frame = self.render.getNextFrame() frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY) if len(self.tracks) > 0: img0, img1 = self.prev_gray, frame_gray p0 = np.float32([tr[-1][0] for tr in self.tracks]).reshape(-1, 1, 2) p1, _st, _err = cv.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params) p0r, _st, _err = cv.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params) d = abs(p0-p0r).reshape(-1, 2).max(-1) good = d < 1 new_tracks = [] for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good): if not good_flag: continue tr.append([(x, y), self.frame_idx]) if len(tr) > self.track_len: del tr[0] new_tracks.append(tr) self.tracks = new_tracks if self.frame_idx % self.detect_interval == 0: goodTracksCount = 0 for tr in self.tracks: oldRect = self.render.getRectInTime(self.render.timeStep * tr[0][1]) newRect = self.render.getRectInTime(self.render.timeStep * tr[-1][1]) if isPointInRect(tr[0][0], oldRect) and isPointInRect(tr[-1][0], newRect): goodTracksCount += 1 if self.frame_idx == self.detect_interval: foregroundPointsNum = goodTracksCount fgIndex = float(foregroundPointsNum) / (foregroundPointsNum + 1) fgRate = float(goodTracksCount) / (len(self.tracks) + 1) if self.frame_idx > 0: self.assertGreater(fgIndex, 0.9) self.assertGreater(fgRate, 0.2) mask = np.zeros_like(frame_gray) mask[:] = 255 for x, y in [np.int32(tr[-1][0]) for tr in self.tracks]: cv.circle(mask, (x, y), 5, 0, -1) p = cv.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params) if p is not None: for x, y in np.float32(p).reshape(-1, 2): self.tracks.append([[(x, y), self.frame_idx]]) self.frame_idx += 1 self.prev_gray = frame_gray if self.frame_idx > 300: break
def test_lk_homography(self): self.render = TestSceneRender(self.get_sample('samples/python2/data/graf1.png'), self.get_sample('samples/c/box.png'), noise = 0.1, speed = 1.0) frame = self.render.getNextFrame() frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) self.frame0 = frame.copy() self.p0 = cv2.goodFeaturesToTrack(frame_gray, **feature_params) isForegroundHomographyFound = False if self.p0 is not None: self.p1 = self.p0 self.gray0 = frame_gray self.gray1 = frame_gray currRect = self.render.getCurrentRect() for (x,y) in self.p0[:,0]: if isPointInRect((x,y), currRect): self.numFeaturesInRectOnStart += 1 while self.framesCounter < 200: self.framesCounter += 1 frame = self.render.getNextFrame() frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if self.p0 is not None: p2, trace_status = checkedTrace(self.gray1, frame_gray, self.p1) self.p1 = p2[trace_status].copy() self.p0 = self.p0[trace_status].copy() self.gray1 = frame_gray if len(self.p0) < 4: self.p0 = None continue H, status = cv2.findHomography(self.p0, self.p1, cv2.RANSAC, 5.0) goodPointsInRect = 0 goodPointsOutsideRect = 0 for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]): if good: if isPointInRect((x1,y1), self.render.getCurrentRect()): goodPointsInRect += 1 else: goodPointsOutsideRect += 1 if goodPointsOutsideRect < goodPointsInRect: isForegroundHomographyFound = True self.assertGreater(float(goodPointsInRect) / (self.numFeaturesInRectOnStart + 1), 0.6) else: p = cv2.goodFeaturesToTrack(frame_gray, **feature_params) self.assertEqual(isForegroundHomographyFound, True)